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Original Paper: https://arxiv.org/abs/2408.04560 By: Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector, Shai Gretz, Lena Dankin, Alon Halfon, Yoav Katz, Noam Slonim Abstract: Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often
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Original Paper: https://arxiv.org/abs/2408.03314 By: Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar Abstract: Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study
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Original Paper: https://arxiv.org/abs/2408.02545 By: Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat, Peter Izsak Abstract: Implementing Retrieval-Augmented Generation (RAG) systems is inherently complex, requiring deep understanding of data, use cases, and intricate design decisions. Additionally, evaluating these systems presents significant challenges, necessitating assessment of both retrieval accuracy
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Original Paper: https://arxiv.org/abs/2408.02479 By: Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, Huaming Chen Abstract: With the rise of large language models (LLMs), researchers are increasingly exploring their applications in var ious vertical domains, such as software engineering. LLMs have achieved remarkable success
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Original Paper: https://arxiv.org/abs/2408.02479 By: Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, Huaming Chen Abstract: With the rise of large language models (LLMs), researchers are increasingly exploring their applications in var ious vertical domains, such as software engineering. LLMs have achieved remarkable success
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Original Paper: https://arxiv.org/abs/2408.02442 By: Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen Abstract: Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large
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Original Paper: https://arxiv.org/abs/2407.21772 By: Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, Oscar Wahltinez Abstract: We present ShieldGemma, a comprehensive suite of LLM-based safety content moderation models built upon Gemma2. These
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Original Paper: https://arxiv.org/abs/2407.19813 By: Yuan Xia, Jingbo Zhou, Zhenhui Shi, Jun Chen, Haifeng Huang Abstract: The Retrieval-Augmented Language Model (RALM) has shown remarkable performance on knowledge-intensive tasks by incorporating external knowledge during inference, which mitigates the factual hallucinations inherited in large language models (LLMs). Despite
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Original Paper: https://arxiv.org/abs/2407.13692 By: Jan Hendrik Kirchner, Yining Chen, Harri Edwards, Jan Leike, Nat McAleese, Yuri Burda Abstract: One way to increase confidence in the outputs of Large Language Models (LLMs) is to support them with reasoning that is clear and easy to check – a
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Original Paper: https://arxiv.org/abs/2407.21712 By: Xi Wang, Procheta Sen, Ruizhe Li, Emine Yilmaz Abstract: Despite the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses. Hence, many existing
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Original Paper: https://arxiv.org/abs/2407.20516 By: Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, Meng Jiang Abstract: Generative AI technologies have been deployed in many places, such as (multimodal) large language models and vision generative models. Their remarkable performance should be attributed to massive training data and
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Original Paper: https://arxiv.org/abs/2407.19594 By: Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar Abstract: Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding